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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.ml.classification
import org.apache.spark.annotation.AlphaComponent
import org.apache.spark.ml._
import org.apache.spark.ml.param._
import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
import org.apache.spark.mllib.linalg.{BLAS, Vector, VectorUDT}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.analysis.Star
import org.apache.spark.sql.catalyst.dsl._
import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
import org.apache.spark.storage.StorageLevel
/**
* :: AlphaComponent ::
* Params for logistic regression.
*/
@AlphaComponent
private[classification] trait LogisticRegressionParams extends Params
with HasRegParam with HasMaxIter with HasLabelCol with HasThreshold with HasFeaturesCol
with HasScoreCol with HasPredictionCol {
/**
* Validates and transforms the input schema with the provided param map.
* @param schema input schema
* @param paramMap additional parameters
* @param fitting whether this is in fitting
* @return output schema
*/
protected def validateAndTransformSchema(
schema: StructType,
paramMap: ParamMap,
fitting: Boolean): StructType = {
val map = this.paramMap ++ paramMap
val featuresType = schema(map(featuresCol)).dataType
// TODO: Support casting Array[Double] and Array[Float] to Vector.
require(featuresType.isInstanceOf[VectorUDT],
s"Features column ${map(featuresCol)} must be a vector column but got $featuresType.")
if (fitting) {
val labelType = schema(map(labelCol)).dataType
require(labelType == DoubleType,
s"Cannot convert label column ${map(labelCol)} of type $labelType to a double column.")
}
val fieldNames = schema.fieldNames
require(!fieldNames.contains(map(scoreCol)), s"Score column ${map(scoreCol)} already exists.")
require(!fieldNames.contains(map(predictionCol)),
s"Prediction column ${map(predictionCol)} already exists.")
val outputFields = schema.fields ++ Seq(
StructField(map(scoreCol), DoubleType, false),
StructField(map(predictionCol), DoubleType, false))
StructType(outputFields)
}
}
/**
* Logistic regression.
*/
class LogisticRegression extends Estimator[LogisticRegressionModel] with LogisticRegressionParams {
setRegParam(0.1)
setMaxIter(100)
setThreshold(0.5)
def setRegParam(value: Double): this.type = set(regParam, value)
def setMaxIter(value: Int): this.type = set(maxIter, value)
def setLabelCol(value: String): this.type = set(labelCol, value)
def setThreshold(value: Double): this.type = set(threshold, value)
def setFeaturesCol(value: String): this.type = set(featuresCol, value)
def setScoreCol(value: String): this.type = set(scoreCol, value)
def setPredictionCol(value: String): this.type = set(predictionCol, value)
override def fit(dataset: SchemaRDD, paramMap: ParamMap): LogisticRegressionModel = {
transformSchema(dataset.schema, paramMap, logging = true)
import dataset.sqlContext._
val map = this.paramMap ++ paramMap
val instances = dataset.select(map(labelCol).attr, map(featuresCol).attr)
.map { case Row(label: Double, features: Vector) =>
LabeledPoint(label, features)
}.persist(StorageLevel.MEMORY_AND_DISK)
val lr = new LogisticRegressionWithLBFGS
lr.optimizer
.setRegParam(map(regParam))
.setNumIterations(map(maxIter))
val lrm = new LogisticRegressionModel(this, map, lr.run(instances).weights)
instances.unpersist()
// copy model params
Params.inheritValues(map, this, lrm)
lrm
}
private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
validateAndTransformSchema(schema, paramMap, fitting = true)
}
}
/**
* :: AlphaComponent ::
* Model produced by [[LogisticRegression]].
*/
@AlphaComponent
class LogisticRegressionModel private[ml] (
override val parent: LogisticRegression,
override val fittingParamMap: ParamMap,
weights: Vector)
extends Model[LogisticRegressionModel] with LogisticRegressionParams {
def setThreshold(value: Double): this.type = set(threshold, value)
def setFeaturesCol(value: String): this.type = set(featuresCol, value)
def setScoreCol(value: String): this.type = set(scoreCol, value)
def setPredictionCol(value: String): this.type = set(predictionCol, value)
private[ml] override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
validateAndTransformSchema(schema, paramMap, fitting = false)
}
override def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD = {
transformSchema(dataset.schema, paramMap, logging = true)
import dataset.sqlContext._
val map = this.paramMap ++ paramMap
val score: Vector => Double = (v) => {
val margin = BLAS.dot(v, weights)
1.0 / (1.0 + math.exp(-margin))
}
val t = map(threshold)
val predict: Double => Double = (score) => {
if (score > t) 1.0 else 0.0
}
dataset.select(Star(None), score.call(map(featuresCol).attr) as map(scoreCol))
.select(Star(None), predict.call(map(scoreCol).attr) as map(predictionCol))
}
}
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